Overview

Dataset statistics

Number of variables26
Number of observations130
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.5 KiB
Average record size in memory209.0 B

Variable types

Numeric19
Categorical7

Warnings

PLAYER NAME has a high cardinality: 130 distinct values High cardinality
RUNS-C is highly correlated with WKTSHigh correlation
WKTS is highly correlated with RUNS-CHigh correlation
AVE-BL is highly correlated with SR-BLHigh correlation
SR-BL is highly correlated with AVE-BLHigh correlation
Sl.NO. is uniformly distributed Uniform
PLAYER NAME is uniformly distributed Uniform
Sl.NO. has unique values Unique
PLAYER NAME has unique values Unique
T-RUNS has 28 (21.5%) zeros Zeros
T-WKTS has 45 (34.6%) zeros Zeros
ODI-RUNS-S has 9 (6.9%) zeros Zeros
ODI-SR-B has 9 (6.9%) zeros Zeros
ODI-WKTS has 34 (26.2%) zeros Zeros
ODI-SR-BL has 34 (26.2%) zeros Zeros
RUNS-S has 2 (1.5%) zeros Zeros
HS has 2 (1.5%) zeros Zeros
AVE has 3 (2.3%) zeros Zeros
SR-B has 2 (1.5%) zeros Zeros
SIXERS has 26 (20.0%) zeros Zeros
RUNS-C has 36 (27.7%) zeros Zeros
WKTS has 41 (31.5%) zeros Zeros
AVE-BL has 41 (31.5%) zeros Zeros
ECON has 36 (27.7%) zeros Zeros
SR-BL has 41 (31.5%) zeros Zeros

Reproduction

Analysis started2021-02-09 12:47:20.551325
Analysis finished2021-02-09 12:51:52.929159
Duration4 minutes and 32.38 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Sl.NO.
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct130
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.5
Minimum1
Maximum130
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-02-09T18:21:54.525291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.45
Q133.25
median65.5
Q397.75
95-th percentile123.55
Maximum130
Range129
Interquartile range (IQR)64.5

Descriptive statistics

Standard deviation37.67182855
Coefficient of variation (CV)0.5751424207
Kurtosis-1.2
Mean65.5
Median Absolute Deviation (MAD)32.5
Skewness0
Sum8515
Variance1419.166667
MonotocityStrictly increasing
2021-02-09T18:21:55.509681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1301
 
0.8%
331
 
0.8%
351
 
0.8%
361
 
0.8%
371
 
0.8%
381
 
0.8%
391
 
0.8%
401
 
0.8%
411
 
0.8%
421
 
0.8%
Other values (120)120
92.3%
ValueCountFrequency (%)
11
0.8%
21
0.8%
31
0.8%
41
0.8%
51
0.8%
ValueCountFrequency (%)
1301
0.8%
1291
0.8%
1281
0.8%
1271
0.8%
1261
0.8%

PLAYER NAME
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct130
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Flintoff, A
 
1
Chanderpaul, S
 
1
Malinga, SL
 
1
Kaif, M
 
1
Sharma, J
 
1
Other values (125)
125 

Length

Max length17
Median length10
Mean length10.58461538
Min length6

Characters and Unicode

Total characters1376
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)100.0%

Sample

1st rowAbdulla, YA
2nd rowAbdur Razzak
3rd rowAgarkar, AB
4th rowAshwin, R
5th rowBadrinath, S
ValueCountFrequency (%)
Flintoff, A1
 
0.8%
Chanderpaul, S1
 
0.8%
Malinga, SL1
 
0.8%
Kaif, M1
 
0.8%
Sharma, J1
 
0.8%
Smith, DR1
 
0.8%
Zaheer Khan1
 
0.8%
Noffke, AA1
 
0.8%
Morkel, JA1
 
0.8%
Patel, MM1
 
0.8%
Other values (120)120
92.3%
2021-02-09T18:21:57.383461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a6
 
2.3%
m5
 
1.9%
s5
 
1.9%
ab4
 
1.5%
singh4
 
1.5%
khan3
 
1.1%
sharma3
 
1.1%
pp2
 
0.8%
morkel2
 
0.8%
sm2
 
0.8%
Other values (208)227
86.3%

Most occurring characters

ValueCountFrequency (%)
a142
 
10.3%
133
 
9.7%
,114
 
8.3%
r66
 
4.8%
i62
 
4.5%
n60
 
4.4%
e55
 
4.0%
h54
 
3.9%
S44
 
3.2%
l41
 
3.0%
Other values (43)605
44.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter765
55.6%
Uppercase Letter361
26.2%
Space Separator133
 
9.7%
Other Punctuation114
 
8.3%
Dash Punctuation3
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
a142
18.6%
r66
 
8.6%
i62
 
8.1%
n60
 
7.8%
e55
 
7.2%
h54
 
7.1%
l41
 
5.4%
o34
 
4.4%
s31
 
4.1%
t29
 
3.8%
Other values (16)191
25.0%
ValueCountFrequency (%)
S44
12.2%
M36
 
10.0%
A31
 
8.6%
P27
 
7.5%
D25
 
6.9%
K24
 
6.6%
R22
 
6.1%
J18
 
5.0%
B16
 
4.4%
H15
 
4.2%
Other values (14)103
28.5%
ValueCountFrequency (%)
,114
100.0%
ValueCountFrequency (%)
133
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1126
81.8%
Common250
 
18.2%

Most frequent character per script

ValueCountFrequency (%)
a142
 
12.6%
r66
 
5.9%
i62
 
5.5%
n60
 
5.3%
e55
 
4.9%
h54
 
4.8%
S44
 
3.9%
l41
 
3.6%
M36
 
3.2%
o34
 
3.0%
Other values (40)532
47.2%
ValueCountFrequency (%)
133
53.2%
,114
45.6%
-3
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1376
100.0%

Most frequent character per block

ValueCountFrequency (%)
a142
 
10.3%
133
 
9.7%
,114
 
8.3%
r66
 
4.8%
i62
 
4.5%
n60
 
4.4%
e55
 
4.0%
h54
 
3.9%
S44
 
3.2%
l41
 
3.0%
Other values (43)605
44.0%

AGE
Categorical

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
86 
3
28 
1
16 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2
ValueCountFrequency (%)
286
66.2%
328
 
21.5%
116
 
12.3%
2021-02-09T18:21:59.320987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-09T18:21:59.789752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
286
66.2%
328
 
21.5%
116
 
12.3%

Most occurring characters

ValueCountFrequency (%)
286
66.2%
328
 
21.5%
116
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number130
100.0%

Most frequent character per category

ValueCountFrequency (%)
286
66.2%
328
 
21.5%
116
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common130
100.0%

Most frequent character per script

ValueCountFrequency (%)
286
66.2%
328
 
21.5%
116
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII130
100.0%

Most frequent character per block

ValueCountFrequency (%)
286
66.2%
328
 
21.5%
116
 
12.3%

COUNTRY
Categorical

Distinct10
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
IND
53 
AUS
22 
SA
16 
SL
12 
PAK
Other values (5)
18 

Length

Max length3
Median length3
Mean length2.684615385
Min length2

Characters and Unicode

Total characters349
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st rowSA
2nd rowBAN
3rd rowIND
4th rowIND
5th rowIND
ValueCountFrequency (%)
IND53
40.8%
AUS22
16.9%
SA16
 
12.3%
SL12
 
9.2%
PAK9
 
6.9%
NZ7
 
5.4%
WI6
 
4.6%
ENG3
 
2.3%
BAN1
 
0.8%
ZIM1
 
0.8%
2021-02-09T18:22:01.586641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-09T18:22:02.008529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ind53
40.8%
aus22
16.9%
sa16
 
12.3%
sl12
 
9.2%
pak9
 
6.9%
nz7
 
5.4%
wi6
 
4.6%
eng3
 
2.3%
zim1
 
0.8%
ban1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
N64
18.3%
I60
17.2%
D53
15.2%
S50
14.3%
A48
13.8%
U22
 
6.3%
L12
 
3.4%
P9
 
2.6%
K9
 
2.6%
Z8
 
2.3%
Other values (5)14
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter349
100.0%

Most frequent character per category

ValueCountFrequency (%)
N64
18.3%
I60
17.2%
D53
15.2%
S50
14.3%
A48
13.8%
U22
 
6.3%
L12
 
3.4%
P9
 
2.6%
K9
 
2.6%
Z8
 
2.3%
Other values (5)14
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin349
100.0%

Most frequent character per script

ValueCountFrequency (%)
N64
18.3%
I60
17.2%
D53
15.2%
S50
14.3%
A48
13.8%
U22
 
6.3%
L12
 
3.4%
P9
 
2.6%
K9
 
2.6%
Z8
 
2.3%
Other values (5)14
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII349
100.0%

Most frequent character per block

ValueCountFrequency (%)
N64
18.3%
I60
17.2%
D53
15.2%
S50
14.3%
A48
13.8%
U22
 
6.3%
L12
 
3.4%
P9
 
2.6%
K9
 
2.6%
Z8
 
2.3%
Other values (5)14
 
4.0%

TEAM
Categorical

Distinct17
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
CSK
14 
KKR+
12 
RCB+
12 
DD+
10 
DC+
10 
Other values (12)
72 

Length

Max length5
Median length3
Mean length3.184615385
Min length2

Characters and Unicode

Total characters414
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowKXIP
2nd rowRCB
3rd rowKKR
4th rowCSK
5th rowCSK
ValueCountFrequency (%)
CSK14
10.8%
KKR+12
 
9.2%
RCB+12
 
9.2%
DD+10
 
7.7%
DC+10
 
7.7%
RR+9
 
6.9%
RCB9
 
6.9%
KXIP+7
 
5.4%
DC7
 
5.4%
MI6
 
4.6%
Other values (7)34
26.2%
2021-02-09T18:22:03.727320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rcb21
16.2%
csk19
14.6%
kkr17
13.1%
dc17
13.1%
dd16
12.3%
rr15
11.5%
kxip12
9.2%
mi12
9.2%
kxi1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
+72
17.4%
R68
16.4%
K66
15.9%
C57
13.8%
D49
11.8%
I25
 
6.0%
B21
 
5.1%
S19
 
4.6%
X13
 
3.1%
P12
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter342
82.6%
Math Symbol72
 
17.4%

Most frequent character per category

ValueCountFrequency (%)
R68
19.9%
K66
19.3%
C57
16.7%
D49
14.3%
I25
 
7.3%
B21
 
6.1%
S19
 
5.6%
X13
 
3.8%
P12
 
3.5%
M12
 
3.5%
ValueCountFrequency (%)
+72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin342
82.6%
Common72
 
17.4%

Most frequent character per script

ValueCountFrequency (%)
R68
19.9%
K66
19.3%
C57
16.7%
D49
14.3%
I25
 
7.3%
B21
 
6.1%
S19
 
5.6%
X13
 
3.8%
P12
 
3.5%
M12
 
3.5%
ValueCountFrequency (%)
+72
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII414
100.0%

Most frequent character per block

ValueCountFrequency (%)
+72
17.4%
R68
16.4%
K66
15.9%
C57
13.8%
D49
11.8%
I25
 
6.0%
B21
 
5.1%
S19
 
4.6%
X13
 
3.1%
P12
 
2.9%

PLAYING ROLE
Categorical

Distinct4
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Bowler
44 
Batsman
39 
Allrounder
35 
W. Keeper
12 

Length

Max length10
Median length7
Mean length7.653846154
Min length6

Characters and Unicode

Total characters995
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAllrounder
2nd rowBowler
3rd rowBowler
4th rowBowler
5th rowBatsman
ValueCountFrequency (%)
Bowler44
33.8%
Batsman39
30.0%
Allrounder35
26.9%
W. Keeper12
 
9.2%
2021-02-09T18:22:05.008570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-09T18:22:05.508594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
bowler44
31.0%
batsman39
27.5%
allrounder35
24.6%
w12
 
8.5%
keeper12
 
8.5%

Most occurring characters

ValueCountFrequency (%)
r126
12.7%
e115
11.6%
l114
11.5%
B83
8.3%
o79
 
7.9%
a78
 
7.8%
n74
 
7.4%
w44
 
4.4%
t39
 
3.9%
s39
 
3.9%
Other values (9)204
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter829
83.3%
Uppercase Letter142
 
14.3%
Other Punctuation12
 
1.2%
Space Separator12
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
r126
15.2%
e115
13.9%
l114
13.8%
o79
9.5%
a78
9.4%
n74
8.9%
w44
 
5.3%
t39
 
4.7%
s39
 
4.7%
m39
 
4.7%
Other values (3)82
9.9%
ValueCountFrequency (%)
B83
58.5%
A35
24.6%
W12
 
8.5%
K12
 
8.5%
ValueCountFrequency (%)
.12
100.0%
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin971
97.6%
Common24
 
2.4%

Most frequent character per script

ValueCountFrequency (%)
r126
13.0%
e115
11.8%
l114
11.7%
B83
8.5%
o79
8.1%
a78
8.0%
n74
7.6%
w44
 
4.5%
t39
 
4.0%
s39
 
4.0%
Other values (7)180
18.5%
ValueCountFrequency (%)
.12
50.0%
12
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII995
100.0%

Most frequent character per block

ValueCountFrequency (%)
r126
12.7%
e115
11.6%
l114
11.5%
B83
8.3%
o79
 
7.9%
a78
 
7.8%
n74
 
7.4%
w44
 
4.4%
t39
 
3.9%
s39
 
3.9%
Other values (9)204
20.5%

T-RUNS
Real number (ℝ≥0)

ZEROS

Distinct103
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2166.715385
Minimum0
Maximum15470
Zeros28
Zeros (%)21.5%
Memory size1.1 KiB
2021-02-09T18:22:06.086707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125.5
median542.5
Q33002.25
95-th percentile9111.55
Maximum15470
Range15470
Interquartile range (IQR)2976.75

Descriptive statistics

Standard deviation3305.646757
Coefficient of variation (CV)1.525648814
Kurtosis3.374525436
Mean2166.715385
Median Absolute Deviation (MAD)542.5
Skewness1.928024455
Sum281673
Variance10927300.48
MonotocityNot monotonic
2021-02-09T18:22:06.758590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
028
 
21.5%
31541
 
0.8%
601
 
0.8%
69731
 
0.8%
631
 
0.8%
3201
 
0.8%
2881
 
0.8%
701
 
0.8%
741
 
0.8%
57081
 
0.8%
Other values (93)93
71.5%
ValueCountFrequency (%)
028
21.5%
51
 
0.8%
111
 
0.8%
131
 
0.8%
161
 
0.8%
ValueCountFrequency (%)
154701
0.8%
132881
0.8%
132181
0.8%
123791
0.8%
104401
0.8%

T-WKTS
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.53076923
Minimum0
Maximum800
Zeros45
Zeros (%)34.6%
Memory size1.1 KiB
2021-02-09T18:22:07.477352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q347.5
95-th percentile376.05
Maximum800
Range800
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation142.6768555
Coefficient of variation (CV)2.144524363
Kurtosis10.23356631
Mean66.53076923
Median Absolute Deviation (MAD)7
Skewness3.104904481
Sum8649
Variance20356.68509
MonotocityNot monotonic
2021-02-09T18:22:08.261724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045
34.6%
16
 
4.6%
25
 
3.8%
55
 
3.8%
74
 
3.1%
94
 
3.1%
322
 
1.5%
212
 
1.5%
62
 
1.5%
1572
 
1.5%
Other values (50)53
40.8%
ValueCountFrequency (%)
045
34.6%
16
 
4.6%
25
 
3.8%
31
 
0.8%
55
 
3.8%
ValueCountFrequency (%)
8001
0.8%
7081
0.8%
6191
0.8%
5631
0.8%
4211
0.8%

ODI-RUNS-S
Real number (ℝ≥0)

ZEROS

Distinct117
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2508.738462
Minimum0
Maximum18426
Zeros9
Zeros (%)6.9%
Memory size1.1 KiB
2021-02-09T18:22:09.011734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q173.25
median835
Q33523.5
95-th percentile10540.2
Maximum18426
Range18426
Interquartile range (IQR)3450.25

Descriptive statistics

Standard deviation3582.205625
Coefficient of variation (CV)1.427891221
Kurtosis3.496939373
Mean2508.738462
Median Absolute Deviation (MAD)817
Skewness1.874177372
Sum326136
Variance12832197.14
MonotocityNot monotonic
2021-02-09T18:22:09.652373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
6.9%
13
 
2.3%
11002
 
1.5%
732
 
1.5%
182
 
1.5%
46861
 
0.8%
64551
 
0.8%
21051
 
0.8%
5751
 
0.8%
33931
 
0.8%
Other values (107)107
82.3%
ValueCountFrequency (%)
09
6.9%
13
 
2.3%
31
 
0.8%
41
 
0.8%
51
 
0.8%
ValueCountFrequency (%)
184261
0.8%
137041
0.8%
134301
0.8%
114981
0.8%
113631
0.8%

ODI-SR-B
Real number (ℝ≥0)

ZEROS

Distinct118
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.16438462
Minimum0
Maximum116.66
Zeros9
Zeros (%)6.9%
Memory size1.1 KiB
2021-02-09T18:22:10.386752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q165.65
median78.225
Q386.79
95-th percentile98.767
Maximum116.66
Range116.66
Interquartile range (IQR)21.14

Descriptive statistics

Standard deviation25.89843966
Coefficient of variation (CV)0.3639241708
Kurtosis1.876015486
Mean71.16438462
Median Absolute Deviation (MAD)9.32
Skewness-1.438179803
Sum9251.37
Variance670.7291768
MonotocityNot monotonic
2021-02-09T18:22:11.074287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
6.9%
1002
 
1.5%
78.962
 
1.5%
502
 
1.5%
602
 
1.5%
27.771
 
0.8%
85.771
 
0.8%
71.231
 
0.8%
78.941
 
0.8%
84.441
 
0.8%
Other values (108)108
83.1%
ValueCountFrequency (%)
09
6.9%
14.281
 
0.8%
27.771
 
0.8%
341
 
0.8%
34.051
 
0.8%
ValueCountFrequency (%)
116.661
0.8%
113.871
0.8%
113.61
0.8%
104.681
0.8%
100.251
0.8%

ODI-WKTS
Real number (ℝ≥0)

ZEROS

Distinct74
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.07692308
Minimum0
Maximum534
Zeros34
Zeros (%)26.2%
Memory size1.1 KiB
2021-02-09T18:22:11.884443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18.5
Q3106
95-th percentile330.7
Maximum534
Range534
Interquartile range (IQR)106

Descriptive statistics

Standard deviation111.2050704
Coefficient of variation (CV)1.461745111
Kurtosis2.803627375
Mean76.07692308
Median Absolute Deviation (MAD)18.5
Skewness1.797747378
Sum9890
Variance12366.56768
MonotocityNot monotonic
2021-02-09T18:22:12.650073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
26.2%
19
 
6.9%
24
 
3.1%
34
 
3.1%
1542
 
1.5%
672
 
1.5%
342
 
1.5%
1002
 
1.5%
162
 
1.5%
1332
 
1.5%
Other values (64)67
51.5%
ValueCountFrequency (%)
034
26.2%
19
 
6.9%
24
 
3.1%
34
 
3.1%
41
 
0.8%
ValueCountFrequency (%)
5341
0.8%
4001
0.8%
3931
0.8%
3811
0.8%
3771
0.8%

ODI-SR-BL
Real number (ℝ≥0)

ZEROS

Distinct82
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.03384615
Minimum0
Maximum150
Zeros34
Zeros (%)26.2%
Memory size1.1 KiB
2021-02-09T18:22:13.406744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36.6
Q345.325
95-th percentile64.74
Maximum150
Range150
Interquartile range (IQR)45.325

Descriptive statistics

Standard deviation26.75174863
Coefficient of variation (CV)0.7860336593
Kurtosis3.527733863
Mean34.03384615
Median Absolute Deviation (MAD)9.5
Skewness1.060627394
Sum4424.4
Variance715.6560549
MonotocityNot monotonic
2021-02-09T18:22:14.582824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
26.2%
423
 
2.3%
42.12
 
1.5%
36.32
 
1.5%
602
 
1.5%
342
 
1.5%
432
 
1.5%
36.62
 
1.5%
41.42
 
1.5%
45.12
 
1.5%
Other values (72)77
59.2%
ValueCountFrequency (%)
034
26.2%
121
 
0.8%
23.41
 
0.8%
27.51
 
0.8%
28.51
 
0.8%
ValueCountFrequency (%)
1501
0.8%
1371
0.8%
1171
0.8%
901
0.8%
86.61
0.8%

CAPTAINCY EXP
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
89 
1
41 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
089
68.5%
141
31.5%
2021-02-09T18:22:16.097729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-09T18:22:16.503988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
089
68.5%
141
31.5%

Most occurring characters

ValueCountFrequency (%)
089
68.5%
141
31.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number130
100.0%

Most frequent character per category

ValueCountFrequency (%)
089
68.5%
141
31.5%

Most occurring scripts

ValueCountFrequency (%)
Common130
100.0%

Most frequent character per script

ValueCountFrequency (%)
089
68.5%
141
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII130
100.0%

Most frequent character per block

ValueCountFrequency (%)
089
68.5%
141
31.5%

RUNS-S
Real number (ℝ≥0)

ZEROS

Distinct115
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514.2461538
Minimum0
Maximum2254
Zeros2
Zeros (%)1.5%
Memory size1.1 KiB
2021-02-09T18:22:17.003995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.45
Q139
median172
Q3925.25
95-th percentile1794.1
Maximum2254
Range2254
Interquartile range (IQR)886.25

Descriptive statistics

Standard deviation615.2263346
Coefficient of variation (CV)1.196365457
Kurtosis0.08308023165
Mean514.2461538
Median Absolute Deviation (MAD)165.5
Skewness1.123951999
Sum66852
Variance378503.4428
MonotocityNot monotonic
2021-02-09T18:22:17.896994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44
 
3.1%
34
 
3.1%
113
 
2.3%
393
 
2.3%
02
 
1.5%
492
 
1.5%
362
 
1.5%
522
 
1.5%
812
 
1.5%
601
 
0.8%
Other values (105)105
80.8%
ValueCountFrequency (%)
02
1.5%
21
 
0.8%
34
3.1%
44
3.1%
61
 
0.8%
ValueCountFrequency (%)
22541
0.8%
20651
0.8%
20471
0.8%
19751
0.8%
19651
0.8%

HS
Real number (ℝ≥0)

ZEROS

Distinct73
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.43076923
Minimum0
Maximum158
Zeros2
Zeros (%)1.5%
Memory size1.1 KiB
2021-02-09T18:22:18.921067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median35.5
Q373.75
95-th percentile109.55
Maximum158
Range158
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation36.40362416
Coefficient of variation (CV)0.7675107267
Kurtosis-0.6117550589
Mean47.43076923
Median Absolute Deviation (MAD)27
Skewness0.5868360157
Sum6166
Variance1325.223852
MonotocityNot monotonic
2021-02-09T18:22:19.929142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37
 
5.4%
715
 
3.8%
165
 
3.8%
244
 
3.1%
114
 
3.1%
154
 
3.1%
753
 
2.3%
23
 
2.3%
1093
 
2.3%
343
 
2.3%
Other values (63)89
68.5%
ValueCountFrequency (%)
02
 
1.5%
23
2.3%
37
5.4%
42
 
1.5%
61
 
0.8%
ValueCountFrequency (%)
1581
0.8%
1281
0.8%
1191
0.8%
1171
0.8%
1161
0.8%

AVE
Real number (ℝ≥0)

ZEROS

Distinct113
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.71930769
Minimum0
Maximum50.11
Zeros3
Zeros (%)2.3%
Memory size1.1 KiB
2021-02-09T18:22:20.987341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.45
Q19.825
median18.635
Q327.8725
95-th percentile36.1525
Maximum50.11
Range50.11
Interquartile range (IQR)18.0475

Descriptive statistics

Standard deviation11.09422422
Coefficient of variation (CV)0.5926621009
Kurtosis-0.8104524672
Mean18.71930769
Median Absolute Deviation (MAD)9.08
Skewness0.1475210944
Sum2433.51
Variance123.0818111
MonotocityNot monotonic
2021-02-09T18:22:21.752977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44
 
3.1%
03
 
2.3%
113
 
2.3%
93
 
2.3%
33
 
2.3%
22.332
 
1.5%
12
 
1.5%
9.92
 
1.5%
27.972
 
1.5%
212
 
1.5%
Other values (103)104
80.0%
ValueCountFrequency (%)
03
2.3%
12
1.5%
1.51
 
0.8%
21
 
0.8%
33
2.3%
ValueCountFrequency (%)
50.111
0.8%
42.271
0.8%
39.921
0.8%
37.911
0.8%
37.131
0.8%

SR-B
Real number (ℝ≥0)

ZEROS

Distinct125
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.0534615
Minimum0
Maximum235.49
Zeros2
Zeros (%)1.5%
Memory size1.1 KiB
2021-02-09T18:22:22.440486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.614
Q198.2375
median118.51
Q3129.1025
95-th percentile158.2125
Maximum235.49
Range235.49
Interquartile range (IQR)30.865

Descriptive statistics

Standard deviation35.92890656
Coefficient of variation (CV)0.3235280203
Kurtosis2.268089265
Mean111.0534615
Median Absolute Deviation (MAD)13.63
Skewness-0.5898743772
Sum14436.95
Variance1290.886327
MonotocityNot monotonic
2021-02-09T18:22:23.269338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
1.5%
802
 
1.5%
131.882
 
1.5%
110.632
 
1.5%
133.332
 
1.5%
114.731
 
0.8%
106.811
 
0.8%
105.621
 
0.8%
165.881
 
0.8%
136.271
 
0.8%
Other values (115)115
88.5%
ValueCountFrequency (%)
02
1.5%
0.751
0.8%
28.571
0.8%
30.31
0.8%
30.771
0.8%
ValueCountFrequency (%)
235.491
0.8%
205.261
0.8%
176.081
0.8%
167.321
0.8%
165.881
0.8%

SIXERS
Real number (ℝ≥0)

ZEROS

Distinct48
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.69230769
Minimum0
Maximum129
Zeros26
Zeros (%)20.0%
Memory size1.1 KiB
2021-02-09T18:22:24.333413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q329.75
95-th percentile65.65
Maximum129
Range129
Interquartile range (IQR)28.75

Descriptive statistics

Standard deviation23.82814639
Coefficient of variation (CV)1.346808274
Kurtosis4.198432421
Mean17.69230769
Median Absolute Deviation (MAD)6
Skewness1.889970957
Sum2300
Variance567.7805605
MonotocityNot monotonic
2021-02-09T18:22:25.193832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
026
20.0%
118
 
13.8%
37
 
5.4%
56
 
4.6%
25
 
3.8%
65
 
3.8%
84
 
3.1%
93
 
2.3%
243
 
2.3%
283
 
2.3%
Other values (38)50
38.5%
ValueCountFrequency (%)
026
20.0%
118
13.8%
25
 
3.8%
37
 
5.4%
42
 
1.5%
ValueCountFrequency (%)
1291
0.8%
971
0.8%
861
0.8%
821
0.8%
811
0.8%

RUNS-C
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct92
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean475.5230769
Minimum0
Maximum1975
Zeros36
Zeros (%)27.7%
Memory size1.1 KiB
2021-02-09T18:22:26.009890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median297
Q3689.25
95-th percentile1690.95
Maximum1975
Range1975
Interquartile range (IQR)689.25

Descriptive statistics

Standard deviation558.3140487
Coefficient of variation (CV)1.174105056
Kurtosis0.3061870503
Mean475.5230769
Median Absolute Deviation (MAD)297
Skewness1.195524991
Sum61818
Variance311714.577
MonotocityNot monotonic
2021-02-09T18:22:26.785918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036
27.7%
1052
 
1.5%
402
 
1.5%
3452
 
1.5%
6271
 
0.8%
5691
 
0.8%
13381
 
0.8%
13421
 
0.8%
3211
 
0.8%
661
 
0.8%
Other values (82)82
63.1%
ValueCountFrequency (%)
036
27.7%
211
 
0.8%
241
 
0.8%
291
 
0.8%
402
 
1.5%
ValueCountFrequency (%)
19751
0.8%
19191
0.8%
18991
0.8%
18921
0.8%
18191
0.8%

WKTS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct51
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.16923077
Minimum0
Maximum83
Zeros41
Zeros (%)31.5%
Memory size1.1 KiB
2021-02-09T18:22:27.519346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.5
Q323.75
95-th percentile67.65
Maximum83
Range83
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation21.8167628
Coefficient of variation (CV)1.270689589
Kurtosis0.8111736185
Mean17.16923077
Median Absolute Deviation (MAD)8.5
Skewness1.376289788
Sum2232
Variance475.9711389
MonotocityNot monotonic
2021-02-09T18:22:28.222474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041
31.5%
65
 
3.8%
24
 
3.1%
134
 
3.1%
94
 
3.1%
73
 
2.3%
363
 
2.3%
123
 
2.3%
83
 
2.3%
103
 
2.3%
Other values (41)57
43.8%
ValueCountFrequency (%)
041
31.5%
13
 
2.3%
24
 
3.1%
31
 
0.8%
42
 
1.5%
ValueCountFrequency (%)
831
0.8%
742
1.5%
731
0.8%
701
0.8%
692
1.5%

AVE-BL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct88
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.11023077
Minimum0
Maximum126.3
Zeros41
Zeros (%)31.5%
Memory size1.1 KiB
2021-02-09T18:22:28.863108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24.785
Q335.58
95-th percentile52.5
Maximum126.3
Range126.3
Interquartile range (IQR)35.58

Descriptive statistics

Standard deviation20.80205724
Coefficient of variation (CV)0.9001233022
Kurtosis3.862907509
Mean23.11023077
Median Absolute Deviation (MAD)13.005
Skewness1.203221134
Sum3004.33
Variance432.7255852
MonotocityNot monotonic
2021-02-09T18:22:29.660001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041
31.5%
52.52
 
1.5%
402
 
1.5%
14.381
 
0.8%
31.361
 
0.8%
37.881
 
0.8%
25.811
 
0.8%
29.921
 
0.8%
23.721
 
0.8%
24.831
 
0.8%
Other values (78)78
60.0%
ValueCountFrequency (%)
041
31.5%
10.81
 
0.8%
12.091
 
0.8%
14.381
 
0.8%
15.331
 
0.8%
ValueCountFrequency (%)
126.31
0.8%
86.251
0.8%
72.51
0.8%
71.21
0.8%
57.51
0.8%

ECON
Real number (ℝ≥0)

ZEROS

Distinct83
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.204461538
Minimum0
Maximum38.11
Zeros36
Zeros (%)27.7%
Memory size1.1 KiB
2021-02-09T18:22:31.347543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.38
Q38.2475
95-th percentile10.308
Maximum38.11
Range38.11
Interquartile range (IQR)8.2475

Descriptive statistics

Standard deviation4.941530545
Coefficient of variation (CV)0.7964479293
Kurtosis12.59788749
Mean6.204461538
Median Absolute Deviation (MAD)1.095
Skewness1.918096068
Sum806.58
Variance24.41872413
MonotocityNot monotonic
2021-02-09T18:22:32.097531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036
27.7%
8.253
 
2.3%
7.112
 
1.5%
7.752
 
1.5%
7.022
 
1.5%
7.892
 
1.5%
7.732
 
1.5%
7.712
 
1.5%
6.852
 
1.5%
9.552
 
1.5%
Other values (73)75
57.7%
ValueCountFrequency (%)
036
27.7%
6.231
 
0.8%
6.361
 
0.8%
6.461
 
0.8%
6.491
 
0.8%
ValueCountFrequency (%)
38.111
0.8%
211
0.8%
14.51
0.8%
141
0.8%
121
0.8%

SR-BL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct82
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.38261538
Minimum0
Maximum100.2
Zeros41
Zeros (%)31.5%
Memory size1.1 KiB
2021-02-09T18:22:32.863168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19.935
Q326.2125
95-th percentile37.232
Maximum100.2
Range100.2
Interquartile range (IQR)26.2125

Descriptive statistics

Standard deviation15.27342169
Coefficient of variation (CV)0.8786607397
Kurtosis5.503533899
Mean17.38261538
Median Absolute Deviation (MAD)8.455
Skewness1.294331573
Sum2259.74
Variance233.2774102
MonotocityNot monotonic
2021-02-09T18:22:33.628803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041
31.5%
333
 
2.3%
243
 
2.3%
122
 
1.5%
28.112
 
1.5%
272
 
1.5%
11.22
 
1.5%
32.771
 
0.8%
21.111
 
0.8%
21.191
 
0.8%
Other values (72)72
55.4%
ValueCountFrequency (%)
041
31.5%
8.41
 
0.8%
11.22
 
1.5%
122
 
1.5%
13.411
 
0.8%
ValueCountFrequency (%)
100.21
0.8%
58.51
0.8%
531
0.8%
441
0.8%
41.331
0.8%

AUCTION YEAR
Categorical

Distinct4
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2008
75 
2011
42 
2009
10 
2010
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters520
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2009
2nd row2008
3rd row2008
4th row2011
5th row2011
ValueCountFrequency (%)
200875
57.7%
201142
32.3%
200910
 
7.7%
20103
 
2.3%
2021-02-09T18:22:35.050699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-09T18:22:35.519449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
200875
57.7%
201142
32.3%
200910
 
7.7%
20103
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0218
41.9%
2130
25.0%
187
 
16.7%
875
 
14.4%
910
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number520
100.0%

Most frequent character per category

ValueCountFrequency (%)
0218
41.9%
2130
25.0%
187
 
16.7%
875
 
14.4%
910
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common520
100.0%

Most frequent character per script

ValueCountFrequency (%)
0218
41.9%
2130
25.0%
187
 
16.7%
875
 
14.4%
910
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII520
100.0%

Most frequent character per block

ValueCountFrequency (%)
0218
41.9%
2130
25.0%
187
 
16.7%
875
 
14.4%
910
 
1.9%

BASE PRICE
Real number (ℝ≥0)

Distinct17
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192230.7692
Minimum20000
Maximum1350000
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-02-09T18:22:35.999348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile50000
Q1100000
median200000
Q3225000
95-th percentile400000
Maximum1350000
Range1330000
Interquartile range (IQR)125000

Descriptive statistics

Standard deviation153097.3009
Coefficient of variation (CV)0.7964245345
Kurtosis28.76874716
Mean192230.7692
Median Absolute Deviation (MAD)50000
Skewness4.400709209
Sum24990000
Variance2.343878354 × 1010
MonotocityNot monotonic
2021-02-09T18:22:36.667661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20000029
22.3%
10000029
22.3%
15000014
10.8%
25000013
10.0%
5000011
 
8.5%
2250007
 
5.4%
4000006
 
4.6%
3000005
 
3.8%
1250004
 
3.1%
200003
 
2.3%
Other values (7)9
 
6.9%
ValueCountFrequency (%)
200003
 
2.3%
5000011
 
8.5%
10000029
22.3%
1250004
 
3.1%
15000014
10.8%
ValueCountFrequency (%)
13500001
 
0.8%
9500001
 
0.8%
4500001
 
0.8%
4000006
4.6%
3500002
 
1.5%

SOLD PRICE
Real number (ℝ≥0)

Distinct53
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521223.0769
Minimum20000
Maximum1800000
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-02-09T18:22:37.355168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile50000
Q1225000
median437500
Q3700000
95-th percentile1527500
Maximum1800000
Range1780000
Interquartile range (IQR)475000

Descriptive statistics

Standard deviation406807.3514
Coefficient of variation (CV)0.780486071
Kurtosis2.004514771
Mean521223.0769
Median Absolute Deviation (MAD)237500
Skewness1.376956313
Sum67759000
Variance1.654922212 × 1011
MonotocityNot monotonic
2021-02-09T18:22:38.245967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500006
 
4.6%
5000006
 
4.6%
1000006
 
4.6%
7000005
 
3.8%
2000005
 
3.8%
1500005
 
3.8%
3500005
 
3.8%
6750005
 
3.8%
8000005
 
3.8%
2250004
 
3.1%
Other values (43)78
60.0%
ValueCountFrequency (%)
200001
 
0.8%
240001
 
0.8%
500006
4.6%
800001
 
0.8%
950001
 
0.8%
ValueCountFrequency (%)
18000004
3.1%
16000001
 
0.8%
15500002
1.5%
15000001
 
0.8%
13500001
 
0.8%

Interactions

2021-02-09T18:17:54.872850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:17:56.678039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:17:57.342086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:17:58.158144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:17:58.830191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:17:59.439071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:00.318269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:01.099434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:01.953224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:02.737270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:03.624727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:04.514424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:05.649409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:06.481469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:07.401541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:08.289594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:08.910533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:09.586720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:10.231201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:11.007246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:11.690715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:12.382383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:12.993766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:13.589564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:14.653080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:15.217672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:15.922413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:16.487222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:17.084386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:17.697588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:18.452347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:19.157502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:19.934794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:20.615697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:21.263079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:21.892491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:22.567007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:23.145020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:23.825093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:24.418938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:25.062024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:25.876587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:26.505802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:27.054566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:27.704838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:28.265375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:28.873444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:29.491854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:30.196970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:30.775811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:31.479840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:32.351922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:33.040799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:33.706767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:34.318234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:34.929858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:35.535257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:36.162393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:36.711250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:37.322573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:37.902650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:38.474709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:39.064426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:39.637131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:40.162509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:40.773883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:41.369612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:41.926234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:42.495031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:43.148309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:43.713573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:44.372270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:44.999244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:45.613618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:46.233914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:46.846968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:47.517880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:48.467207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:49.062982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:49.762073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:50.529745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:51.262859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:51.827333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:52.435950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:53.044979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:53.737641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:54.442918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:55.243407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:56.019281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:56.765237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:57.501538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:58.128407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:58.772802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:59.329387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:18:59.956334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:00.552184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:01.164219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:01.683626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:02.336423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:02.814862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:03.395410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:03.928567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:04.493028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:05.049471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:05.567216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:06.276760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:06.786354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:07.547096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:08.346327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:09.283126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:10.003928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:10.955806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:11.827865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:12.947948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:13.667994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:14.197413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:14.865240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:15.461007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:16.134805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:16.793463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:17.493851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:18.058331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:18.685282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:19.665604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:20.277009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:20.952766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:21.649884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:22.167499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:22.747682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:23.265299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:23.829859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:24.339421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:24.881600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:25.423812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:26.074603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:26.592267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:27.109907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:27.643201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:28.192115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:19:28.717338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-09T18:21:40.795672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:21:41.763739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-09T18:21:42.595151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-09T18:22:39.398073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-09T18:22:41.662202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-09T18:22:43.444988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-09T18:22:45.637186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-09T18:22:47.074726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-09T18:21:44.360797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-09T18:21:50.738445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Sl.NO.PLAYER NAMEAGECOUNTRYTEAMPLAYING ROLET-RUNST-WKTSODI-RUNS-SODI-SR-BODI-WKTSODI-SR-BLCAPTAINCY EXPRUNS-SHSAVESR-BSIXERSRUNS-CWKTSAVE-BLECONSR-BLAUCTION YEARBASE PRICESOLD PRICE
01Abdulla, YA2SAKXIPAllrounder0000.0000.00000.000.0003071520.478.9013.9320095000050000
12Abdur Razzak2BANRCBBowler2141865771.4118537.60000.000.0002900.0014.500.0020085000050000
23Agarkar, AB2INDKKRBowler57158126980.6228832.901673918.56121.01510592936.528.8124.902008200000350000
34Ashwin, R1INDCSKBowler2843124184.565136.8058115.8076.32011254922.966.2322.142011100000850000
45Badrinath, S2INDCSKBatsman6307945.9300.0013177132.93120.7128000.000.000.002011100000800000
56Bailey, GJ2AUSCSKBatsman0017272.2600.01634821.0095.450000.000.000.0020095000050000
67Balaji, L2INDCSK+Bowler512712078.943442.5026154.3372.22113425225.817.9819.402011100000500000
78Bollinger, DE2AUSCSKBowler54505092.596231.30211621.00165.8816933718.737.2215.572011200000700000
89Botha, J2SARRAllrounder831760985.777253.013356730.45114.7336101932.116.8528.112011200000950000
910Boucher, MV2SARCB+W. Keeper55151468684.7600.013945028.14127.5113000.000.000.002008200000450000

Last rows

Sl.NO.PLAYER NAMEAGECOUNTRYTEAMPLAYING ROLET-RUNST-WKTSODI-RUNS-SODI-SR-BODI-WKTSODI-SR-BLCAPTAINCY EXPRUNS-SHSAVESR-BSIXERSRUNS-CWKTSAVE-BLECONSR-BLAUCTION YEARBASE PRICESOLD PRICE
120121Vettori, DL2NZDD+Allrounder4486359210581.9328245.711212915.13107.0828782831.366.8127.752008250000625000
121122Vinay Kumar, R2INDRCB+Bowler1114343.872835.30217259.43104.83516646127.288.2419.872011100000475000
122123Warne, SK3AUSRRBowler3154708101872.0429336.31198349.9092.52614475725.397.2720.952008450000450000
123124Warner, DA1AUSDDBatsman483287685.7900.00102510927.70135.7644000.000.000.002011200000750000
124125White, CL2AUSRCB+Batsman1465203780.481227.517457831.04132.09297000.0014.000.002008100000500000
125126Yadav, AS2INDDCBatsman0000.0000.0049169.80125.642000.000.000.00201050000750000
126127Younis Khan2PAKRRBatsman63987681475.78386.61333.0042.850000.000.000.002008225000225000
127128Yuvraj Singh2INDKXIP+Batsman17759805187.5810944.3112376626.32131.88675692324.747.0221.1320114000001800000
128129Zaheer Khan2INDMI+Bowler111428879073.5527835.4099239.9091.67117836527.437.7521.262008200000450000
129130Zoysa, DNT2SLDCBowler2886434395.8110839.40111011.00122.22099249.509.0033.002008100000110000